Predictors of response to pharmacotherapy in children and adolescents with psychiatric disorders: A combined post hoc analysis of four clinical trial data
Bibliographic record
Abstract
OBJECTIVE: The prediction of response to pharmacotherapy has not been sufficiently explored in children and adolescents with psychiatric disorders, which was addressed in this study. METHODS: Data from four double-blind, placebo-controlled studies (sertraline and fluvoxamine for anxiety disorders, risperidone for autistic disorder, and fluoxetine for major depressive disorder) in children and adolescents funded by the National Institute of Mental Health were used. The response was defined as a score of 1 or 2 on the Clinical Global Impression-Global Improvement (CGI-I) at the endpoint. Logistic regression analysis was performed to evaluate associations between response status and the following variables: sex, diagnosis, treatment allocation, and CGI-Severity of Illness (CGI-S) score at baseline. Moreover, the presence of early improvement (a score of ≤3 in the CGI-I) at Week 1 was added to the independent variables in an additional binary logistic regression analysis, using the data from two studies. RESULTS: A total of 599 patients were included in the analysis. In the binary logistic regression analysis, active drug use (odds ratio [OR] = 8.64, P < 0.001) and female sex (OR = 1.89, P = 0.002) were significantly associated with treatment response. In the second binary logistic regression, the presence of early improvement in the CGI-I (OR = 3.47, P = 0.009), as well as active drug use (OR = 15.05, P < 0.001) and female sex (OR = 2.87, P = 0.016), were associated with subsequent responses. CONCLUSION: Allocation to active drugs, female sex, and early improvement may predict treatment response to pharmacotherapy among children and adolescents with psychiatric disorders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".